Yes and no!  The term “narrative synthesis” is often used when authors do not use meta-analysis to synthesise their data.  This is because the synthesis relies heavily on narrative or text.  However, there is no clear definition of what narrative synthesis or what narrative synthesis involves as a method.  This means that the term can be confusing and opaque.  Indeed, we conducted work that found that the methods of narrative synthesis are rarely reported; this is of course contrary to the application of a scientific principles of transparency that under pin a systematic review approach. (Campbell et al 2019)  The SWiM work was initiated to address the lack of transparency in narrative synthesis, and was originally called “Improving the CONduct and reporting of Narrative Synthesis of Quantitative data (ICONS-Quant).  Over the duration of the project we tried to develop a clear definition of narrative synthesis. But after many lengthy discussions this seemed to be an impossible task.  This is in part because, the term narrative synthesis may be applicable in many, or even most, reviews.  That is, that in most reviews the narrative or textual description of the studies and the synthesis findings plays a key role in presenting the interpretation of the included data, and can be a valuable way to explore and explain relationships across the included studies, in particular when the review includes diverse sources of data.  In addition, the term narrative synthesis is used for a wide range of methods, approaches and include synthesis of different data types, including both quantitative and qualitative data.  As indicated above, the focus of this work was to promote transparency of synthesis of quantitative intervention effect data when meta-analysis is not used: This is only one of the situations in which the term narrative synthesis has been used.  The term SWiM therefore relates specifically to improving transparency of, and pointing authors to conduct guidance on, the synthesis of quantitative intervention effect data when meta-analysis of standardised effect sizes is not used.  This is only one aspect of what has been implied within the wide banner of narrative synthesis.

Have a look at this opinion piece on SWiM for some further thoughts.

No.  The SWiM acronym is used for brevity for the lengthy phrase Synthesis Without Meta-analysis.  SWiM is an umbrella phrase or term which describes a broad approach and may include one or more of a range of different synthesis methods, e.g. synthesis based on effect direction, or summary statistics such as the median.  Currently, we are using SWiM to relate specifically to synthesis of quantitative intervention effect data which does not use meta-analysis of standardised effect sizes.  But this is just the start and the terminology may evolve further, for example there may be scope for extensions to SWiM, as there is definitely potential for more discussion on the use of terminology such as “narrative synthesis”.

There are many possible explanations for the poor reporting in reviews where the synthesis does not use meta-analysis of standardised effect sizes.  One reason may be due to the limited guidance on how to conduct and report effect data when meta-analysis is not used, and also limited guidance on appropriate management in heterogeneity in study characteristics, for example studies which report similar but not the same outcome at the same timepoint.  Another related reason may be that many authors expect and hope that they will conduct a meta-analysis, indeed some authors think that a systematic review needs to include a meta-analysis, which is not the case, around 16% of Cochrane reviews do not include any meta-analysis, and only half of Cochrane reviews rely exclusively on meta-analysis to synthesise effect data. 

For more guidance on managing heterogeneous data in a systematic review, have a look at Cochrane Handbook Chapter 9, “Preparing for Synthesis”.

The term “vote counting” has been associated with bad practice in systematic reviews. Two main reasons why vote counting has been considered bad practice is that it often relies on inappropriate interpretation of statistical significance, ignores risk of bias of included studies, and/or the approach used is not reported transparently.  However, many reviews which do not conduct meta-analysis of standardised effect sizes resort to vote counting even though they may avoid using the term due its negative associations.  The new Cochrane Handbook Chapter “Synthesis using other methods”,outlines various approaches to vote counting, noting that there are different approaches to vote counting.  The advice in the Handbook cautions against approaches which rely on counting studies on the basis of statistical significance.  Currently the most common form of vote counting uses effect direction as the standardised metric.  While vote counting is not so useful or robust a method as a meta-analysis, use of a reliable method of vote counting can allow authors to make best of the available evidence, and avoid concluding that little or nothing is known about the effect of an intervention when there are data on effect direction.